
import numpy as np 
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats 
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR         
from sklearn.pipeline import Pipeline
from sklearn.metrics import r2_score, mean_squared_error
from itertools import product
import time
from matplotlib.dates import MonthLocator, DateFormatter
import plotly.graph_objects as go
from plotly.subplots import make_subplots
!pip install pmdarima
from pandas.plotting import autocorrelation_plot
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
import statsmodels.api as sm
!pip install prophet
from prophet import Prophet
from pmdarima import auto_arima
import statsmodels.api as sm

import warnings
warnings.filterwarnings("ignore")

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[notice] To update, run: python.exe -m pip install --upgrade pip

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[notice] To update, run: python.exe -m pip install --upgrade pip

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Note: you may need to restart the kernel to use updated packages.


df = pd.read_csv("C:/Users/b1247569/OneDrive - Teesside University/OLORO/sales.csv")
df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9800 entries, 0 to 9799
Data columns (total 18 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Row ID         9800 non-null   int64  
 1   Order ID       9800 non-null   object 
 2   Order Date     9800 non-null   object 
 3   Ship Date      9800 non-null   object 
 4   Ship Mode      9800 non-null   object 
 5   Customer ID    9800 non-null   object 
 6   Customer Name  9800 non-null   object 
 7   Segment        9800 non-null   object 
 8   Country        9800 non-null   object 
 9   City           9800 non-null   object 
 10  State          9800 non-null   object 
 11  Postal Code    9789 non-null   float64
 12  Region         9800 non-null   object 
 13  Product ID     9800 non-null   object 
 14  Category       9800 non-null   object 
 15  Sub-Category   9800 non-null   object 
 16  Product Name   9800 non-null   object 
 17  Sales          9800 non-null   float64
dtypes: float64(2), int64(1), object(15)
memory usage: 1.3+ MB


df.dtypes

Row ID             int64
Order ID          object
Order Date        object
Ship Date         object
Ship Mode         object
Customer ID       object
Customer Name     object
Segment           object
Country           object
City              object
State             object
Postal Code      float64
Region            object
Product ID        object
Category          object
Sub-Category      object
Product Name      object
Sales            float64
dtype: object


df.describe()


df.shape

(9800, 18)


# CHECKING FOR MISSING VALUES
df.isna().sum().sum()

11


df.loc[df["Postal Code"].isna(),['Country','City','State','Postal Code']]


df.loc[(df['City']=='Burlington') & (df['State']=='Vermont'),['Country','Postal Code']]


df = df.copy()
df['Postal Code'] = df['Postal Code'].fillna(5401) # leading zeros in decimal integer literals are not permitted so we use 5401


df.isna().sum().sum()

0


df.drop(columns = ['Country','Customer ID'], inplace = True) 

df["Row ID"] = df["Row ID"].astype("str") 
df.loc[(df["Postal Code"].isnull()), "Postal Code"] = 5401 

df["Postal Code"] = df["Postal Code"].astype("str") 

def convert_datetime(dataframe):  
    date_cols = [col for col in dataframe.columns if 'Date' in col]
   
    for col in date_cols:
        dataframe[col] = pd.to_datetime(dataframe[col], errors = 'coerce')
    return dataframe.info()

convert_datetime(df)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9800 entries, 0 to 9799
Data columns (total 16 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Row ID         9800 non-null   object        
 1   Order ID       9800 non-null   object        
 2   Order Date     9800 non-null   datetime64[ns]
 3   Ship Date      9800 non-null   datetime64[ns]
 4   Ship Mode      9800 non-null   object        
 5   Customer Name  9800 non-null   object        
 6   Segment        9800 non-null   object        
 7   City           9800 non-null   object        
 8   State          9800 non-null   object        
 9   Postal Code    9800 non-null   object        
 10  Region         9800 non-null   object        
 11  Product ID     9800 non-null   object        
 12  Category       9800 non-null   object        
 13  Sub-Category   9800 non-null   object        
 14  Product Name   9800 non-null   object        
 15  Sales          9800 non-null   float64       
dtypes: datetime64[ns](2), float64(1), object(13)
memory usage: 1.2+ MB


def grab_col_name(dataframe, cat_th = 5, car_th = 49):
    cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
    num_but_cat = [col for col in dataframe.columns if dataframe[col].dtypes != "O" and dataframe[col].nunique() < cat_th]
    cat_but_car = [col for col in dataframe.columns if dataframe[col].dtypes == "O" and dataframe[col].nunique() > car_th]
    
    cat_cols = cat_cols + num_but_cat
    cat_cols = [col for col in cat_cols if col not in cat_but_car]
    
    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes in ["int64", "float64"] and "ID" not in col.upper()]
    num_cols= [col for col in num_cols if col not in num_but_cat]
    
    date_cols = [col for col in dataframe.columns if 'Date' in col]
    
    for col in date_cols:
            dataframe[col] = pd.to_datetime(dataframe[col], errors = 'coerce')
            
    return cat_cols, num_cols, cat_but_car,date_cols


grab_col_name(df)

(['Ship Mode', 'Segment', 'State', 'Region', 'Category', 'Sub-Category'],
 ['Sales'],
 ['Row ID',
  'Order ID',
  'Customer Name',
  'City',
  'Postal Code',
  'Product ID',
  'Product Name'],
 ['Order Date', 'Ship Date'])


cat_cols, num_cols, cat_but_car, date_cols = grab_col_name(df)


df_1 = df.copy()
df_1['Order Date'] = pd.to_datetime(df['Order Date'], format='%d/%m/%Y')
df_1['Ship Date'] = pd.to_datetime(df['Ship Date'], format='%d/%m/%Y')
df_1['Month-Year'] = df_1['Order Date'].dt.strftime('%b-%Y')
df_1['Year'] = df_1['Order Date'].dt.year
df_1['Month'] = df_1['Order Date'].dt.month_name()

# Create a column for Number of Days require to ship the product.
df_1['ShipInDays'] = (df_1['Ship Date'] - df_1['Order Date']).dt.days

print('Describing Informations About Order Date' , df_1['Order Date'].describe().T, sep = '\n\n', end = '\n\n\n')
print('Describing Informations About Ship Date' , df_1['Ship Date'].describe().T, sep = '\n\n', end = '\n\n')

Describing Informations About Order Date

count                    9800
unique                   1230
top       2017-05-09 00:00:00
freq                       38
first     2015-01-02 00:00:00
last      2018-12-30 00:00:00
Name: Order Date, dtype: object


Describing Informations About Ship Date

count                    9800
unique                   1326
top       2018-09-26 00:00:00
freq                       34
first     2015-01-04 00:00:00
last      2019-05-01 00:00:00
Name: Ship Date, dtype: object


df_1['Segment'].value_counts()
color_list = ['red','green','steelblue','purple','cadetblue','maroon']
df_1['Segment'].value_counts().plot.bar(color = color_list)
plt.xticks(rotation=360);


df_1['Category'].value_counts()


df_1['Category'].value_counts().plot.bar(color = color_list)
plt.xticks(rotation=60);


df_1['Sub-Category'].value_counts()

df_1['Sub-Category'].value_counts().plot.bar(color = color_list)
plt.xticks(rotation=60);


import plotly.express as px
# Summarize the Sales data by Category and Sub-Category

custom_colors = ["#FF5733", "#33FF57", "#3366FF", "#FF33CC"]
df_1_summary = df_1.groupby(['Category', 'Sub-Category'])['Sales'].sum().reset_index()
# Create a nested pie chart
fig = px.sunburst(
    df_1_summary,
    path=['Category', 'Sub-Category'],
    values='Sales',
     color='Sales',
    color_discrete_sequence=custom_colors,

)

fig.show()


categorical_columns = ['Ship Mode', 'Segment', 'State','Region', 'Category', 'Sub-Category', 'Customer Name', 'City']

def cat_summary(dataframe, plot=False, categorical_columns = cat_cols, threshold=10):
    for col_name in categorical_columns:
        print("#" * 30 + " Unique Values Of " + col_name + " column " + "#" * 30)
        print(col_name + ": " + str(dataframe[col_name].nunique()))
        
        print("#" * 30 + " Frequencies of Class Values " + "#" * 30)
        value_counts = dataframe[col_name].value_counts()
        if dataframe[col_name].nunique() > threshold:
            top_n_values = value_counts.head(threshold)
            #other_count = value_counts[threshold:].sum()
            #top_n_values['Others'] = other_count
            value_counts = top_n_values
        
        print(pd.DataFrame({col_name: value_counts,
                            "Ratio": value_counts / len(dataframe[col_name])}))
        
        if plot:
            rgb_values = sns.color_palette("Set3", 6)
            sns.set_theme(style="darkgrid")
            
            if dataframe[col_name].nunique() > threshold:
                plt.figure(figsize=(13,6))  
            
            ax = sns.barplot(x=value_counts.index, y=value_counts.values, palette=rgb_values)
            ax.set_xticklabels(ax.get_xticklabels(), rotation=60)
            for p in ax.patches:
                ax.annotate(f'\n{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='top', color='white', size=10)
            plt.show()
            
def num_summary(dataframe, plot = False):
    quantiles = [0, 0.25, 0.50, 0.75, 1.0]
    
    for col_name in num_cols:
        print("#"*30 + " Distribution of " + col_name+ " column" + "#"*30)
        print(dataframe[col_name].describe(quantiles).T)
        
        if plot:
            sns.histplot(data = dataframe, x = col_name)
            plt.xlabel(col_name)
            plt.title("The Distribution of "+ col_name)
            plt.grid(True, alpha = 0.5)
            plt.show(block = True)
            
def autopct_format(values): 
    def my_format(pct): 
        total = sum(values) 
        val = int(round(pct*total/100.0))
        return ' ${v:d}'.format(v=val)
    return my_format


cat_summary(df_1,True,categorical_columns = categorical_columns,threshold = 10)

############################## Unique Values Of Ship Mode column ##############################
Ship Mode: 4
############################## Frequencies of Class Values ##############################
                Ship Mode     Ratio
Standard Class       5859  0.597857
Second Class         1902  0.194082
First Class          1501  0.153163
Same Day              538  0.054898

############################## Unique Values Of Segment column ##############################
Segment: 3
############################## Frequencies of Class Values ##############################
             Segment     Ratio
Consumer        5101  0.520510
Corporate       2953  0.301327
Home Office     1746  0.178163

############################## Unique Values Of State column ##############################
State: 49
############################## Frequencies of Class Values ##############################
                State     Ratio
California       1946  0.198571
New York         1097  0.111939
Texas             973  0.099286
Pennsylvania      582  0.059388
Washington        504  0.051429
Illinois          483  0.049286
Ohio              454  0.046327
Florida           373  0.038061
Michigan          253  0.025816
North Carolina    247  0.025204

############################## Unique Values Of Region column ##############################
Region: 4
############################## Frequencies of Class Values ##############################
         Region     Ratio
West       3140  0.320408
East       2785  0.284184
Central    2277  0.232347
South      1598  0.163061

############################## Unique Values Of Category column ##############################
Category: 3
############################## Frequencies of Class Values ##############################
                 Category     Ratio
Office Supplies      5909  0.602959
Furniture            2078  0.212041
Technology           1813  0.185000

############################## Unique Values Of Sub-Category column ##############################
Sub-Category: 17
############################## Frequencies of Class Values ##############################
             Sub-Category     Ratio
Binders              1492  0.152245
Paper                1338  0.136531
Furnishings           931  0.095000
Phones                876  0.089388
Storage               832  0.084898
Art                   785  0.080102
Accessories           756  0.077143
Chairs                607  0.061939
Appliances            459  0.046837
Labels                357  0.036429

############################## Unique Values Of Customer Name column ##############################
Customer Name: 793
############################## Frequencies of Class Values ##############################
                     Customer Name     Ratio
William Brown                   35  0.003571
Matt Abelman                    34  0.003469
Paul Prost                      34  0.003469
John Lee                        33  0.003367
Chloris Kastensmidt             32  0.003265
Jonathan Doherty                32  0.003265
Seth Vernon                     32  0.003265
Emily Phan                      31  0.003163
Zuschuss Carroll                31  0.003163
Arthur Prichep                  31  0.003163

############################## Unique Values Of City column ##############################
City: 529
############################## Frequencies of Class Values ##############################
               City     Ratio
New York City   891  0.090918
Los Angeles     728  0.074286
Philadelphia    532  0.054286
San Francisco   500  0.051020
Seattle         426  0.043469
Houston         374  0.038163
Chicago         308  0.031429
Columbus        221  0.022551
San Diego       170  0.017347
Springfield     161  0.016429


# Summarize the Sales data by Category, Ship Mode, and Sub-Category
df_sum = df_1.groupby(['Category', 'Ship Mode', 'Sub-Category'])['Sales'].sum().reset_index()

# Create a treemap
fig = px.treemap(
    df_sum,
    path=['Category', 'Ship Mode', 'Sub-Category'],
    values='Sales',
)

fig.show()


import plotly.io as pio
pio.renderers.default = 'notebook_connected'


df_1.groupby("Year")["Sales"].sum().plot()
plt.ylabel("Sales")
plt.title("Trend of Total sales over the years")

Text(0.5, 1.0, 'Trend of Total sales over the years')


# Create a mapping for all 50 states 
all_state_mapping = {
    "Alabama": "AL", "Alaska": "AK", "Arizona": "AZ", "Arkansas": "AR",
    "California": "CA", "Colorado": "CO", "Connecticut": "CT", "Delaware": "DE",
    "Florida": "FL", "Georgia": "GA", "Hawaii": "HI", "Idaho": "ID", "Illinois": "IL",
    "Indiana": "IN", "Iowa": "IA", "Kansas": "KS", "Kentucky": "KY", "Louisiana": "LA",
    "Maine": "ME", "Maryland": "MD", "Massachusetts": "MA", "Michigan": "MI", "Minnesota": "MN",
    "Mississippi": "MS", "Missouri": "MO", "Montana": "MT", "Nebraska": "NE", "Nevada": "NV",
    "New Hampshire": "NH", "New Jersey": "NJ", "New Mexico": "NM", "New York": "NY",
    "North Carolina": "NC", "North Dakota": "ND", "Ohio": "OH", "Oklahoma": "OK",
    "Oregon": "OR", "Pennsylvania": "PA", "Rhode Island": "RI", "South Carolina": "SC",
    "South Dakota": "SD", "Tennessee": "TN", "Texas": "TX", "Utah": "UT", "Vermont": "VT",
    "Virginia": "VA", "Washington": "WA", "West Virginia": "WV", "Wisconsin": "WI", "Wyoming": "WY"
}

# Add the Abbreviation column to the DataFrame
df['Abbreviation'] = df_1['State'].map(all_state_mapping)

# Group by state and calculate the sum of sales
sum_of_sales = df_1.groupby('State')['Sales'].sum().reset_index()

# Add Abbreviation to sum_of_sales
sum_of_sales['Abbreviation'] = sum_of_sales['State'].map(all_state_mapping)

# Create a choropleth map using Plotly
fig = go.Figure(data=go.Choropleth(
    locations=sum_of_sales['Abbreviation'],
    locationmode='USA-states',
    z=sum_of_sales['Sales'],
    hoverinfo='location+z',
    showscale=True
))

fig.update_geos(projection_type="albers usa")
fig.update_layout(
    geo_scope='usa',
    title='Total Sales by United States'
)

fig.show()


Year_sales_trend = df_1.groupby(['Year', 'Month']).sum()['Sales'].reset_index()
Year_sales_trend


df_1['Month-Year'] = df_1['Order Date'].dt.strftime('%b-%Y')

Monthly_sales = df_1.groupby('Month-Year').agg({'Sales': 'sum'}).reset_index()
Monthly_sales['Order'] = pd.to_datetime(Monthly_sales['Month-Year'], format='%b-%Y')
Monthly_sales = Monthly_sales.sort_values('Order')


fig = px.line(Monthly_sales, x='Month-Year', y='Sales', title='Sales Trend Over the Years',
              labels={'Month-Year': 'Month-Year', 'Sales': 'Sales'})

fig.update_traces(hovertemplate='Date: %{x}<br>Sales: %{y:,.2f}<extra></extra>')


fig.update_xaxes(tickangle=90, tickmode='array', tickvals=Monthly_sales['Month-Year'],
                 ticktext=Monthly_sales['Month-Year'])


fig.update_layout(width=900, height=600)


fig.show()


def time_breakdown(dataframe = df_1, date_col = 'Order Date',categorical_columns = cat_cols, threshold = 10):
    dataframe[f'Month_Year_{date_col}'] = dataframe[date_col].dt.strftime('%b-%Y')
    for cat_col in categorical_columns:
        if dataframe[cat_col].nunique() <= threshold:
            Monthly_sales = dataframe.groupby([f'Month_Year_{date_col}',cat_col]).agg({'Sales': 'sum'}).reset_index()
            Monthly_sales['Order'] = pd.to_datetime(Monthly_sales[f'Month_Year_{date_col}'], format='%b-%Y')
            Monthly_sales = Monthly_sales.sort_values('Order')


            fig = px.line(Monthly_sales, x=f'Month_Year_{date_col}', y='Sales', title='Monthly Observation of Sales',
              labels={f'Month-Year_{date_col}': f'Month_Year_{date_col}', 'Sales': 'Sales'}, color = cat_col)

            fig.update_traces(hovertemplate='Date: %{x}<br>Sales: %{y:,.2f}<extra></extra>')


            fig.update_xaxes(tickangle=90, tickmode='array', tickvals=Monthly_sales[f'Month_Year_{date_col}'],
                     ticktext=Monthly_sales[f'Month_Year_{date_col}'])


            fig.update_layout(width=1000, height=600)


            fig.show()
            
time_breakdown(df_1, 'Order Date', categorical_columns = categorical_columns, threshold = 10)


df_1['WeekDay'] = df_1['Order Date'].dt.day_name()
start_date = df_1['Order Date'].min()
df_1['Week'] = ((((df_1['Order Date'] - start_date).dt.days)/7) +1).astype(int)
df_plot = df_1.groupby('Order Date').agg({'Sales':'sum'}).rename(columns = {'Sales':'Total Sales'}).reset_index().sort_values(by = 'Order Date', ascending = True)

fig = px.line(df_plot, x="Order Date", y="Total Sales", title='Total Sales by Date', color_discrete_sequence=['red'])
fig.show()


df_1.Region.unique()

array(['South', 'West', 'Central', 'East'], dtype=object)


df_1['Order Date']

0      2017-08-11
1      2017-08-11
2      2017-12-06
3      2016-11-10
4      2016-11-10
          ...    
9795   2017-05-21
9796   2016-12-01
9797   2016-12-01
9798   2016-12-01
9799   2016-12-01
Name: Order Date, Length: 9800, dtype: datetime64[ns]


order_date_df = df_1.set_index('Order Date') # We create a new df but this has 'Order Date' as index, that will help us

# Quarterly sales
quarterly_sales = order_date_df['Sales'].resample('Q').sum()
quarterly_sales = quarterly_sales.round(2)

plt.figure(figsize=(10, 6))
plt.plot(quarterly_sales.index, quarterly_sales.values, marker='o', linestyle='-')

# Customize x-axis ticks for quarterly intervals
ax = plt.gca()
ax.xaxis.set_major_locator(MonthLocator(bymonth=[1, 4, 7, 10]))
ax.xaxis.set_major_formatter(DateFormatter('%b %Y'))

plt.title('Quarterly Sales Trend')
plt.xlabel('Date')
plt.ylabel('Total Sales')
plt.xticks(rotation=60)
plt.grid(True)
plt.show()


# In the first section i created a order_date_df, i can use it to solve this problem
west_df = order_date_df.loc[order_date_df['Region'] == 'West']
east_df = order_date_df.loc[order_date_df['Region'] == 'East']
south_df = order_date_df.loc[order_date_df['Region'] == 'South']
central_df = order_date_df.loc[order_date_df['Region'] == 'Central']

west_monthly_sales = west_df['Sales'].resample('M').sum()
west_monthly_sales = west_monthly_sales.round(2)

east_monthly_sales = east_df['Sales'].resample('M').sum()
east_monthly_sales = east_monthly_sales.round(2)

south_monthly_sales = south_df['Sales'].resample('M').sum()
south_monthly_sales = south_monthly_sales.round(2)

central_monthly_sales = central_df['Sales'].resample('M').sum()
central_monthly_sales = central_monthly_sales.round(2)

fig, (ax_west, ax_east, ax_south, ax_central) = plt.subplots(nrows=4, ncols=1, figsize=(12, 20))

ax_west.plot(west_monthly_sales.index, west_monthly_sales.values, marker='o', linestyle='-')
ax_east.plot(east_monthly_sales.index, east_monthly_sales.values, marker='o', linestyle='-')
ax_south.plot(south_monthly_sales.index, south_monthly_sales.values, marker='o', linestyle='-')
ax_central.plot(central_monthly_sales.index, central_monthly_sales.values, marker='o', linestyle='-')

ax_west.set_title('West Region Monthly Sales')
ax_east.set_title('East Region Monthly Sales')
ax_south.set_title('South Region Monthly Sales')
ax_central.set_title('Central Region Monthly Sales')
ax_west.set_ylabel('Total Sales ($)')
ax_central.set_xlabel('Months')
plt.show()


Sales_trend = df_1[['Month-Year','Sales']].groupby('Month-Year').sum()
Sales_trend = Sales_trend.sort_values(by='Month-Year')
Sales_trend.head()


day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

df_plot_dayofweek = df_1[['WeekDay', 'Sales', 'Order ID']].groupby(['WeekDay']).agg({'Sales': 'sum', 'Order ID': 'count'}).rename(columns = {'Sales': 'Total Sales', 'Order ID':'Order Count'}).reset_index()

df_plot_dayofweek = df_plot_dayofweek.set_index('WeekDay').loc[day_order].reset_index()

fig_dayofweek = px.bar(df_plot_dayofweek, x = 'WeekDay', y = 'Order Count',hover_data=['WeekDay', 'Order Count'], 
                       labels={'WeekDay': 'WeekDay', 'Order Count': 'Order Count'}, 
                       color = 'Order Count', color_discrete_sequence=['green'])
    
fig_dayofweek.update_traces(hovertemplate='WeekDay: %{x}<br>Order Count: %{y}<extra></extra>')

fig_dayofweek.update_layout(title='Average Order Count by Day Of Week',width=1000, height=400)

fig_dayofweek.update_traces(text=df_plot_dayofweek['Order Count'], 
                  texttemplate='%{text:.2f}', 
                  textfont=dict(color='maroon', size=14, family='Arial'),
                  textangle=0,textposition='outside')
fig_dayofweek.show()


day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

df_plot_dayofweek = df_1[['WeekDay', 'Sales', 'Order ID']].groupby(['WeekDay']).agg({'Sales': 'sum', 'Order ID': 'count'}).rename(columns = {'Sales': 'Total Sales', 'Order ID':'Order Count'}).reset_index()

df_plot_dayofweek = df_plot_dayofweek.set_index('WeekDay').loc[day_order].reset_index()

fig_dayofweek = px.bar(df_plot_dayofweek, x = 'WeekDay', y = 'Total Sales',hover_data=['WeekDay', 'Total Sales'], 
                       labels={'WeekDay': 'WeekDay', 'Total Sales': 'Total Sales'}, 
                    color = 'Total Sales')
    
fig_dayofweek.update_traces(hovertemplate='WeekDay: %{x}<br>Total Sales: $%{y}<extra></extra>')

fig_dayofweek.update_layout(title='Total Daily Sales',width=1000, height=400)

fig_dayofweek.update_traces(text=df_plot_dayofweek['Total Sales'], 
                  texttemplate='$%{text:.2f}', 
                  textfont=dict(color='maroon', size=14, family='Arial'),
                  textangle=0,textposition='outside')
fig_dayofweek.show()


Total_Product = df_1['Product Name'].value_counts().head(10)
Total_Product

Staple envelope                                              47
Staples                                                      46
Easy-staple paper                                            44
Avery Non-Stick Binders                                      20
Staples in misc. colors                                      18
Staple remover                                               18
Storex Dura Pro Binders                                      17
KI Adjustable-Height Table                                   17
Staple-based wall hangings                                   16
Logitech 910-002974 M325 Wireless Mouse for Web Scrolling    15
Name: Product Name, dtype: int64


plt.figure(figsize=(12,6))
df_1['Product Name'].value_counts().head(25).sort_values().plot.barh(color = 'red')
plt.xlabel('Count');


#Intall the library
!pip install statsmodels

Requirement already satisfied: statsmodels in c:\programdata\anaconda3\lib\site-packages (0.14.0)
Requirement already satisfied: numpy>=1.18 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (1.24.3)
Requirement already satisfied: scipy!=1.9.2,>=1.4 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (1.10.1)
Requirement already satisfied: pandas>=1.0 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (1.5.3)
Requirement already satisfied: patsy>=0.5.2 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (0.5.3)
Requirement already satisfied: packaging>=21.3 in c:\programdata\anaconda3\lib\site-packages (from statsmodels) (23.0)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\programdata\anaconda3\lib\site-packages (from pandas>=1.0->statsmodels) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\programdata\anaconda3\lib\site-packages (from pandas>=1.0->statsmodels) (2022.7)
Requirement already satisfied: six in c:\programdata\anaconda3\lib\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)


#Loading the library
from statsmodels.tsa.seasonal import STL


date_rng = pd.date_range(start='2015-01-01', end='2018-12-31', freq='D')
data = np.sin(np.arange(len(date_rng)) * (2 * np.pi / 365)) + np.random.normal(scale=0.2, size=len(date_rng))
df_2 = pd.DataFrame(data, columns=['value'], index=date_rng)

# Performing STL decomposition
stl = STL(df_2['value'], seasonal=13)  # The 'seasonal' parameter represents the length of the seasonal component
result = stl.fit()

# Ploting the original time series, trend, seasonal, and residual components
plt.figure(figsize=(12, 8))

plt.subplot(4, 1, 1)
plt.plot(df_2['value'], label='Original Time Series')
plt.legend()

plt.subplot(4, 1, 2)
plt.plot(result.trend, label='Trend')
plt.legend()

plt.subplot(4, 1, 3)
plt.plot(result.seasonal, label='Seasonal')
plt.legend()

plt.subplot(4, 1, 4)
plt.plot(result.resid, label='Residual')
plt.legend()

plt.show()


# use adfuller to check if sales trend is stationary
from statsmodels.tsa.stattools import adfuller


result = adfuller(df_2,maxlag=30)
print('ADF Statistic: %f' % result[0])
print('p-value: %f' % result[1])
print('Critical Values:')
for key, value in result[4].items():
    print('\t%s: %.3f' % (key, value))
    
if result[0]<=-2.9 :
    print('==> Non-stationarity can be rejected')
else :
    print('==> Non-stationarity cannot be rejected')

ADF Statistic: -3.799596
p-value: 0.002911
Critical Values:
	1%: -3.435
	5%: -2.864
	10%: -2.568
==> Non-stationarity can be rejected


# autocorrelation
autocorrelation_plot(df_2)

<Axes: xlabel='Lag', ylabel='Autocorrelation'>


fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(15,6))
plot_pacf(df_2,lags=76,ax=axes[0])
plot_acf(df_2,lags=76,ax=axes[1])
fig.suptitle('Cycle composant')
plt.show()


df_prophet = df_1[["Order Date","Sales"]]
df_prophet


df_prophet.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9800 entries, 0 to 9799
Data columns (total 2 columns):
 #   Column      Non-Null Count  Dtype         
---  ------      --------------  -----         
 0   Order Date  9800 non-null   datetime64[ns]
 1   Sales       9800 non-null   float64       
dtypes: datetime64[ns](1), float64(1)
memory usage: 153.2 KB


# Resample the data to monthly frequency and sum the sales for each month
df_prophet = df.resample('M', on='Order Date').sum()

# Rename columns as 'ds' and 'y' as required by Prophet
df_prophet.reset_index(inplace=True)
df_prophet  = df_prophet.rename(columns = {'Order Date': 'ds','Sales': 'y'})
df_prophet


model = Prophet()
model.fit(df_prophet)

09:57:28 - cmdstanpy - INFO - Chain [1] start processing
09:57:29 - cmdstanpy - INFO - Chain [1] done processing

<prophet.forecaster.Prophet at 0x1f674a4be20>


# Forcasting into the future
future = model.make_future_dataframe(periods=12,freq='M')
forecast = model.predict(future)
forecast.head

<bound method NDFrame.head of            ds         trend    yhat_lower     yhat_upper   trend_lower  \
0  2015-01-31  31723.677206  15006.356871   37032.112393  31723.677206   
1  2015-02-28  32268.101497   8552.404217   28402.314791  32268.101497   
2  2015-03-31  32870.856972  44886.010314   66519.024689  32870.856972   
3  2015-04-30  33454.168722   9660.483331   29818.524622  33454.168722   
4  2015-05-31  34056.924192  15979.867986   38566.339617  34056.924192   
5  2015-06-30  34640.235925  15559.182623   36962.866203  34640.235925   
6  2015-07-31  35242.991382  21856.083267   42657.945103  35242.991382   
7  2015-08-31  35845.746837  26807.328512   47015.252175  35845.746837   
8  2015-09-30  36429.058562  40696.651110   62725.750821  36429.058562   
9  2015-10-31  37031.814012  23656.804604   45254.930529  37031.814012   
10 2015-11-30  37615.125745  52073.978430   73926.036917  37615.125745   
11 2015-12-31  38217.881197  40204.928877   61804.513462  38217.881197   
12 2016-01-31  38820.636648  18454.535438   39744.784662  38820.636648   
13 2016-02-29  39384.504662  22591.345995   43242.695748  39384.504662   
14 2016-03-31  39987.260128  24881.817273   46591.044475  39987.260128   
15 2016-04-30  40570.571870  27667.414915   47963.650967  40570.571870   
16 2016-05-31  41173.327329  29906.277292   51876.770770  41173.327329   
17 2016-06-30  41756.639550  20579.466917   41550.025516  41756.639550   
18 2016-07-31  42359.395513  22835.007459   44295.785886  42359.395513   
19 2016-08-31  42962.151523  41672.318866   63000.994470  42962.151523   
20 2016-09-30  43545.463785  46901.334717   68252.088398  43545.463785   
21 2016-10-31  44148.219789  33104.494984   53906.179115  44148.219789   
22 2016-11-30  44731.532064  46751.247406   68218.543045  44731.532064   
23 2016-12-31  45334.288090  47388.062381   68295.922197  45334.288090   
24 2017-01-31  45937.044116  37145.188936   59332.203533  45937.044116   
25 2017-02-28  46481.468916  26362.130327   48664.178164  46481.468916   
26 2017-03-31  47084.224954  41667.843152   62803.245417  47084.224954   
27 2017-04-30  47667.537249  30244.995647   52165.355270  47667.537249   
28 2017-05-31  48270.293285  35881.602203   56351.770232  48270.293285   
29 2017-06-30  48853.605571  27811.533927   50327.773858  48853.605571   
30 2017-07-31  49456.361599  32060.525197   52275.402060  49456.361599   
31 2017-08-31  50059.117635  45945.454839   67439.455429  50059.117635   
32 2017-09-30  50642.429939  53876.291388   74762.218987  50642.429939   
33 2017-10-31  51245.185986  38376.447904   60387.636708  51245.185986   
34 2017-11-30  51828.498304  57788.877007   79617.578431  51828.498304   
35 2017-12-31  52431.254368  53854.327188   75876.555098  52431.254368   
36 2018-01-31  53034.010432  41102.943559   61620.240745  53034.010432   
37 2018-02-28  53578.435261  30740.861748   53025.769335  53578.435261   
38 2018-03-31  54181.191323  57838.883217   78469.797318  54181.191323   
39 2018-04-30  54764.503642  33876.521430   55463.123077  54764.503642   
40 2018-05-31  55367.259705  40991.666762   61744.985709  55367.259705   
41 2018-06-30  55950.572024  36246.133521   57509.231563  55950.572024   
42 2018-07-31  56553.328087  40610.247086   62149.936687  56553.328087   
43 2018-08-31  57156.084149  49781.216736   72224.887582  57156.084149   
44 2018-09-30  57739.396468  61704.648043   82659.580623  57739.396468   
45 2018-10-31  58342.152531  45218.284538   67048.925864  58342.152531   
46 2018-11-30  58925.464850  69235.572485   90406.875691  58925.464850   
47 2018-12-31  59528.220912  61743.742485   83023.348161  59528.220912   
48 2019-01-31  60130.976975  44906.319845   65794.761511  60130.976952   
49 2019-02-28  60675.401806  36743.092773   56946.391261  60675.401729   
50 2019-03-31  61278.157869  73454.077678   94902.270980  61278.157711   
51 2019-04-30  61861.470187  36999.546413   58585.549314  61861.469945   
52 2019-05-31  62464.226250  45520.098238   65856.792554  62464.225892   
53 2019-06-30  63047.538569  43318.369018   65162.828079  63047.538094   
54 2019-07-31  63650.294632  50516.450095   71220.957439  63650.294012   
55 2019-08-31  64253.050694  54844.637373   75851.424967  64253.049931   
56 2019-09-30  64836.363013  69585.499379   89995.858060  64836.362109   
57 2019-10-31  65439.119076  52110.153935   72981.631416  65439.118025   
58 2019-11-30  66022.431395  79867.017164  100589.686764  66022.430167   
59 2019-12-31  66625.187458  68206.583630   90253.776625  66625.186054   

     trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \
0   31723.677206    -5289.803789          -5289.803789          -5289.803789   
1   32268.101497   -13757.282250         -13757.282250         -13757.282250   
2   32870.856972    23251.311018          23251.311018          23251.311018   
3   33454.168722   -13740.533677         -13740.533677         -13740.533677   
4   34056.924192    -6612.285538          -6612.285538          -6612.285538   
5   34640.235925    -8521.987972          -8521.987972          -8521.987972   
6   35242.991382    -3031.646480          -3031.646480          -3031.646480   
7   35845.746837     1177.670385           1177.670385           1177.670385   
8   36429.058562    15123.717893          15123.717893          15123.717893   
9   37031.814012    -2752.296241          -2752.296241          -2752.296241   
10  37615.125745    24639.634132          24639.634132          24639.634132   
11  38217.881197    12437.323213          12437.323213          12437.323213   
12  38820.636648    -9158.680156          -9158.680156          -9158.680156   
13  39384.504662    -6378.621889          -6378.621889          -6378.621889   
14  39987.260128    -4196.584961          -4196.584961          -4196.584961   
15  40570.571870    -3132.965308          -3132.965308          -3132.965308   
16  41173.327329      214.806408            214.806408            214.806408   
17  41756.639550   -10997.618516         -10997.618516         -10997.618516   
18  42359.395513    -9055.074116          -9055.074116          -9055.074116   
19  42962.151523     9614.666830           9614.666830           9614.666830   
20  43545.463785    13301.094766          13301.094766          13301.094766   
21  44148.219789     -647.667276           -647.667276           -647.667276   
22  44731.532064    13145.713847          13145.713847          13145.713847   
23  45334.288090    12796.473614          12796.473614          12796.473614   
24  45937.044116     2353.141505           2353.141505           2353.141505   
25  46481.468916    -9009.797131          -9009.797131          -9009.797131   
26  47084.224954     4953.274859           4953.274859           4953.274859   
27  47667.537249    -6533.200433          -6533.200433          -6533.200433   
28  48270.293285    -2082.107981          -2082.107981          -2082.107981   
29  48853.605571   -10224.975211         -10224.975211         -10224.975211   
30  49456.361599    -7014.529353          -7014.529353          -7014.529353   
31  50059.117635     6820.969528           6820.969528           6820.969528   
32  50642.429939    13874.319832          13874.319832          13874.319832   
33  51245.185986    -1404.118434          -1404.118434          -1404.118434   
34  51828.498304    17002.050587          17002.050587          17002.050587   
35  52431.254368    12796.457438          12796.457438          12796.457438   
36  53034.010432    -1450.685944          -1450.685944          -1450.685944   
37  53578.435261   -11469.722201         -11469.722201         -11469.722201   
38  54181.191323    14105.641794          14105.641794          14105.641794   
39  54764.503642   -10069.860923         -10069.860923         -10069.860923   
40  55367.259705    -4358.903028          -4358.903028          -4358.903028   
41  55950.572024    -9399.524666          -9399.524666          -9399.524666   
42  56553.328087    -5006.131302          -5006.131302          -5006.131302   
43  57156.084149     4007.806711           4007.806711           4007.806711   
44  57739.396468    14481.827963          14481.827963          14481.827963   
45  58342.152531    -2105.505418          -2105.505418          -2105.505418   
46  58925.464850    20834.815521          20834.815521          20834.815521   
47  59528.220912    12676.589444          12676.589444          12676.589444   
48  60130.976998    -5289.803789          -5289.803789          -5289.803789   
49  60675.401875   -13757.282250         -13757.282250         -13757.282250   
50  61278.158011    23251.311018          23251.311018          23251.311018   
51  61861.470436   -13740.533677         -13740.533677         -13740.533677   
52  62464.226613    -6612.285538          -6612.285538          -6612.285538   
53  63047.539053    -8521.987972          -8521.987972          -8521.987972   
54  63650.295244    -3031.646480          -3031.646480          -3031.646480   
55  64253.051454     1177.670385           1177.670385           1177.670385   
56  64836.363937    15123.717893          15123.717893          15123.717893   
57  65439.120171    -2752.296241          -2752.296241          -2752.296241   
58  66022.432649    24639.634132          24639.634132          24639.634132   
59  66625.188917    12437.323213          12437.323213          12437.323213   

          yearly  yearly_lower  yearly_upper  multiplicative_terms  \
0   -5289.803789  -5289.803789  -5289.803789                   0.0   
1  -13757.282250 -13757.282250 -13757.282250                   0.0   
2   23251.311018  23251.311018  23251.311018                   0.0   
3  -13740.533677 -13740.533677 -13740.533677                   0.0   
4   -6612.285538  -6612.285538  -6612.285538                   0.0   
5   -8521.987972  -8521.987972  -8521.987972                   0.0   
6   -3031.646480  -3031.646480  -3031.646480                   0.0   
7    1177.670385   1177.670385   1177.670385                   0.0   
8   15123.717893  15123.717893  15123.717893                   0.0   
9   -2752.296241  -2752.296241  -2752.296241                   0.0   
10  24639.634132  24639.634132  24639.634132                   0.0   
11  12437.323213  12437.323213  12437.323213                   0.0   
12  -9158.680156  -9158.680156  -9158.680156                   0.0   
13  -6378.621889  -6378.621889  -6378.621889                   0.0   
14  -4196.584961  -4196.584961  -4196.584961                   0.0   
15  -3132.965308  -3132.965308  -3132.965308                   0.0   
16    214.806408    214.806408    214.806408                   0.0   
17 -10997.618516 -10997.618516 -10997.618516                   0.0   
18  -9055.074116  -9055.074116  -9055.074116                   0.0   
19   9614.666830   9614.666830   9614.666830                   0.0   
20  13301.094766  13301.094766  13301.094766                   0.0   
21   -647.667276   -647.667276   -647.667276                   0.0   
22  13145.713847  13145.713847  13145.713847                   0.0   
23  12796.473614  12796.473614  12796.473614                   0.0   
24   2353.141505   2353.141505   2353.141505                   0.0   
25  -9009.797131  -9009.797131  -9009.797131                   0.0   
26   4953.274859   4953.274859   4953.274859                   0.0   
27  -6533.200433  -6533.200433  -6533.200433                   0.0   
28  -2082.107981  -2082.107981  -2082.107981                   0.0   
29 -10224.975211 -10224.975211 -10224.975211                   0.0   
30  -7014.529353  -7014.529353  -7014.529353                   0.0   
31   6820.969528   6820.969528   6820.969528                   0.0   
32  13874.319832  13874.319832  13874.319832                   0.0   
33  -1404.118434  -1404.118434  -1404.118434                   0.0   
34  17002.050587  17002.050587  17002.050587                   0.0   
35  12796.457438  12796.457438  12796.457438                   0.0   
36  -1450.685944  -1450.685944  -1450.685944                   0.0   
37 -11469.722201 -11469.722201 -11469.722201                   0.0   
38  14105.641794  14105.641794  14105.641794                   0.0   
39 -10069.860923 -10069.860923 -10069.860923                   0.0   
40  -4358.903028  -4358.903028  -4358.903028                   0.0   
41  -9399.524666  -9399.524666  -9399.524666                   0.0   
42  -5006.131302  -5006.131302  -5006.131302                   0.0   
43   4007.806711   4007.806711   4007.806711                   0.0   
44  14481.827963  14481.827963  14481.827963                   0.0   
45  -2105.505418  -2105.505418  -2105.505418                   0.0   
46  20834.815521  20834.815521  20834.815521                   0.0   
47  12676.589444  12676.589444  12676.589444                   0.0   
48  -5289.803789  -5289.803789  -5289.803789                   0.0   
49 -13757.282250 -13757.282250 -13757.282250                   0.0   
50  23251.311018  23251.311018  23251.311018                   0.0   
51 -13740.533677 -13740.533677 -13740.533677                   0.0   
52  -6612.285538  -6612.285538  -6612.285538                   0.0   
53  -8521.987972  -8521.987972  -8521.987972                   0.0   
54  -3031.646480  -3031.646480  -3031.646480                   0.0   
55   1177.670385   1177.670385   1177.670385                   0.0   
56  15123.717893  15123.717893  15123.717893                   0.0   
57  -2752.296241  -2752.296241  -2752.296241                   0.0   
58  24639.634132  24639.634132  24639.634132                   0.0   
59  12437.323213  12437.323213  12437.323213                   0.0   

    multiplicative_terms_lower  multiplicative_terms_upper          yhat  
0                          0.0                         0.0  26433.873416  
1                          0.0                         0.0  18510.819247  
2                          0.0                         0.0  56122.167990  
3                          0.0                         0.0  19713.635045  
4                          0.0                         0.0  27444.638653  
5                          0.0                         0.0  26118.247953  
6                          0.0                         0.0  32211.344902  
7                          0.0                         0.0  37023.417222  
8                          0.0                         0.0  51552.776455  
9                          0.0                         0.0  34279.517771  
10                         0.0                         0.0  62254.759877  
11                         0.0                         0.0  50655.204409  
12                         0.0                         0.0  29661.956492  
13                         0.0                         0.0  33005.882773  
14                         0.0                         0.0  35790.675167  
15                         0.0                         0.0  37437.606561  
16                         0.0                         0.0  41388.133736  
17                         0.0                         0.0  30759.021034  
18                         0.0                         0.0  33304.321397  
19                         0.0                         0.0  52576.818353  
20                         0.0                         0.0  56846.558551  
21                         0.0                         0.0  43500.552513  
22                         0.0                         0.0  57877.245911  
23                         0.0                         0.0  58130.761704  
24                         0.0                         0.0  48290.185621  
25                         0.0                         0.0  37471.671785  
26                         0.0                         0.0  52037.499813  
27                         0.0                         0.0  41134.336816  
28                         0.0                         0.0  46188.185304  
29                         0.0                         0.0  38628.630360  
30                         0.0                         0.0  42441.832246  
31                         0.0                         0.0  56880.087163  
32                         0.0                         0.0  64516.749771  
33                         0.0                         0.0  49841.067552  
34                         0.0                         0.0  68830.548891  
35                         0.0                         0.0  65227.711806  
36                         0.0                         0.0  51583.324488  
37                         0.0                         0.0  42108.713059  
38                         0.0                         0.0  68286.833117  
39                         0.0                         0.0  44694.642719  
40                         0.0                         0.0  51008.356676  
41                         0.0                         0.0  46551.047358  
42                         0.0                         0.0  51547.196785  
43                         0.0                         0.0  61163.890860  
44                         0.0                         0.0  72221.224431  
45                         0.0                         0.0  56236.647113  
46                         0.0                         0.0  79760.280370  
47                         0.0                         0.0  72204.810356  
48                         0.0                         0.0  54841.173186  
49                         0.0                         0.0  46918.119556  
50                         0.0                         0.0  84529.468886  
51                         0.0                         0.0  48120.936510  
52                         0.0                         0.0  55851.940712  
53                         0.0                         0.0  54525.550597  
54                         0.0                         0.0  60618.648151  
55                         0.0                         0.0  65430.721080  
56                         0.0                         0.0  79960.080906  
57                         0.0                         0.0  62686.822835  
58                         0.0                         0.0  90662.065527  
59                         0.0                         0.0  79062.510670  >


# plot daily sales
ax = df_prophet.set_index('ds').plot(figsize = (12, 4))
ax.set_ylabel('Daily Number of Sales')
ax.set_xlabel('Date')
plt.show()


# Plot the forecast results
plot_1 = model.plot(forecast)
sns.scatterplot(x='ds', y='y', data=df_prophet, color = 'r', label='True Daily close price')

<Axes: xlabel='ds', ylabel='y'>


# Extract forecasted values
forecasted_data = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

# Plot the forecasted values
plt.figure(figsize=(10, 6))
plt.plot(df_prophet['ds'], df_prophet['y'], label='Observed', color='blue')
plt.plot(forecasted_data['ds'], forecasted_data['yhat'], label='Forecast', color='red')
plt.fill_between(forecasted_data['ds'], forecasted_data['yhat_lower'], forecasted_data['yhat_upper'], color='pink', alpha=0.5)
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Forecast with Line Plot')
plt.legend()
plt.show()


# Extract the actual data and forecast for the next 12 months
actual_data = df_prophet['y']
fitted_data = forecast['yhat'][:len(df_prophet)]  # Fitted values for the historical data
forecast_data = forecast['yhat'][len(df_prophet):]


# Calculate R-squared score for the fitted data
r2_fitted = 1 - np.sum((actual_data - fitted_data) ** 2) / np.sum((actual_data - actual_data.mean()) ** 2)


# Plot the actual data, fitted data, and forecast for the next 12 months
plt.figure(figsize=(12, 6))
plt.plot(df_prophet['ds'], actual_data, label='Monthly Sales (Actual Data)', color='green')
plt.plot(df_prophet['ds'], fitted_data, label='Fitted Data', color='blue')
plt.plot(forecast['ds'][len(df_prophet):], forecast_data, label='Forecast (Next 12 Months)', color='red')

plt.legend()
plt.xlabel('Order Date')
plt.ylabel('Monthly Sales')
plt.title('Prophet Model Forecast')
plt.show()

# Display R-squared score for the fitted data
print(f"R-squared score for fitted data: {r2_fitted:.2f}")

R-squared score for fitted data: 0.75


plot_2 = model.plot_components(forecast)


# Calculate RMSE
rmse = np.sqrt(np.mean((actual_data - fitted_data) ** 2))

# Calculate R-squared score for the fitted data
r2_fitted = 1 - np.sum((actual_data - fitted_data) ** 2) / np.sum((actual_data - actual_data.mean()) ** 2)

# Calculate MAPE
mape = np.mean(np.abs((actual_data - fitted_data) / actual_data)) * 100

# Display R-squared score for the fitted data
print(f"R-squared score for fitted data: {r2_fitted:.2f}")

print(f"RMSE: {rmse:.2f}")
print(f"MAPE: {mape:.2f}%")

R-squared score for fitted data: 0.75
RMSE: 8337.57
MAPE: 14.79%


from statsmodels.tsa.holtwinters import ExponentialSmoothing
import warnings
# Suppress warnings
warnings.filterwarnings("ignore")


df_winter = df_1[["Order Date","Sales"]]
df_winter


# Set 'Order Date' as the index
df.set_index('Order Date', inplace=True)

# Resample the data to monthly frequency and sum the sales for each month
df_winter = df['Sales'].resample('M').sum()


# Split the data into training and test sets (80/20 split)
train_size = int(len(df_winter) * 0.8)
train = df_winter[:train_size]
test = df_winter[train_size:]

param_grid = {
    'seasonal': ['add', 'multiplicative'],
    'seasonal_periods': [12],
    'trend': ['add', 'additive', 'multiplicative'],
    'smoothing_level': [0.1, 0.2, 0.3, 0.4, 0.5],
    'smoothing_trend': [0.1, 0.2, 0.3, 0.4, 0.5],
    'smoothing_seasonal': [0.1, 0.2, 0.3, 0.4, 0.5],
    'damping_trend': [0.1, 0.2, 0.3, 0.4, 0.5],
}

best_model = None
best_mse = float('inf')

total_iterations = len(param_grid['seasonal']) * len(param_grid['seasonal_periods']) * len(param_grid['trend']) * \
                   len(param_grid['smoothing_level']) * \
                   len(param_grid['smoothing_trend']) * len(param_grid['smoothing_seasonal']) * len(param_grid['damping_trend'])

iteration = 0


# Perform a grid search by iterating over hyperparameters
for seasonal in param_grid['seasonal']:
    for seasonal_period in param_grid['seasonal_periods']:
        for trend in param_grid['trend']:
                for smoothing_level in param_grid['smoothing_level']:
                        for smoothing_trend in param_grid['smoothing_trend']:
                            for smoothing_seasonal in param_grid['smoothing_seasonal']:
                                for damping_trend in param_grid['damping_trend']:
                                    iteration += 1

                                    model = ExponentialSmoothing(train, seasonal=seasonal, seasonal_periods=seasonal_period, trend=trend)
                                    model_fit = model.fit(smoothing_level=smoothing_level,smoothing_trend=smoothing_trend, smoothing_seasonal=smoothing_seasonal,damping_slope=damping_trend)
                                    forecast = model_fit.forecast(steps=len(test))
                                    mse = np.mean((test - forecast) ** 2)

                                    # Check if this model has a lower MSE than the best found so far
                                    if mse < best_mse:
                                        best_mse = mse
                                        best_model = model_fit

                                    #print(f"Iteration {iteration}/{total_iterations} - Best MSE: {best_mse:.4f}")

# The best model and its hyperparameters
print("Best Model Hyperparameters:")
print("Seasonal:", best_model.model.seasonal)
print("Seasonal Periods:", best_model.model.seasonal_periods)
print("Trend:", best_model.model.trend)
print("Smoothing Level(alpha):", best_model.params['smoothing_level'])
print("Smoothing Trend(beta):", best_model.params['smoothing_trend'])
print("Smoothing Seasonal(gamma):", best_model.params['smoothing_seasonal'])
print("Damping Trend:", best_model.params['damping_trend'])

Best Model Hyperparameters:
Seasonal: add
Seasonal Periods: 12
Trend: add
Smoothing Level(alpha): 0.1
Smoothing Trend(beta): 0.1
Smoothing Seasonal(gamma): 0.1
Damping Trend: nan


# Best hyperparameters defined after fine tuning the holt-winter
alpha = 0.1  # Smoothing parameter for the level component
beta = 0.1  # Smoothing parameter for the trend component
gamma = 0.1 # Smoothing parameter for the seasonal component
damped = False # Whether the trend component should be damped

# Initialize the ExponentialSmoothing model with hyperparameters
model = ExponentialSmoothing(train, seasonal='add', seasonal_periods=12, trend='add', damped=damped)

# Fit the model with the specified hyperparameters
model_fit = model.fit(smoothing_level=alpha, smoothing_trend=beta, smoothing_seasonal=gamma)

# Print the summary of the model
print(model_fit.summary())

# Make out-of-sample forecasts for the test set
forecast = model_fit.forecast(steps=len(test))


fitted = model_fit.fittedvalues  # Fitted values for training data

# Create an index for the forecasted data starting right after training data
forecast.index = pd.date_range(start=train.index[-1] + pd.DateOffset(months=1), periods=len(test), freq='M')

# Extend the index for a 12-month forecast after the test data
forecast_extended = pd.date_range(start=test.index[-1] + pd.DateOffset(months=1), periods=12, freq='M')
forecast = pd.concat([forecast, pd.Series(data=np.nan, index=forecast_extended)])

# Plot the actual data, training (fitted) data, and forecasted values with different colors
plt.figure(figsize=(12, 6))
plt.plot(df_winter.index, df_winter, label='Actual Data', color='green')
plt.plot(train.index, fitted, label='Training (Fitted) Data', color='blue')
plt.plot(forecast.index, forecast, label='Forecasted Test Data', color='red')

# Get the last value of the test data
last_test_value = test[-1]



# Extend the forecast for an additional 12 months after the test data ends
forecast_extended = model_fit.forecast(steps=12)
forecast_extended.index = pd.date_range(start=test.index[-1] + pd.DateOffset(months=1), periods=12, freq='M')

# Set the first value of the extended forecast to be the last value of the test data
forecast_extended.iloc[0] = last_test_value

plt.plot(forecast_extended.index, forecast_extended, label='12 Months Extended Forecast', color='purple')

plt.legend()
plt.xlabel('Order Date (Monthly)')
plt.ylabel('Sales')
plt.title('Holt-Winters Model (Add Seasonality)')
plt.show()

                       ExponentialSmoothing Model Results                       
================================================================================
Dep. Variable:                    Sales   No. Observations:                   38
Model:             ExponentialSmoothing   SSE                     3547040022.370
Optimized:                         True   AIC                            729.368
Trend:                         Additive   BIC                            755.570
Seasonal:                      Additive   AICC                           765.368
Seasonal Periods:                    12   Date:                 Sun, 12 May 2024
Box-Cox:                          False   Time:                         20:53:51
Box-Cox Coeff.:                    None                                         
=================================================================================
                          coeff                 code              optimized      
---------------------------------------------------------------------------------
smoothing_level               0.1000000                alpha                False
smoothing_trend               0.1000000                 beta                False
smoothing_seasonal            0.1000000                gamma                False
initial_level                 34899.958                  l.0                 True
initial_trend                 391.69283                  b.0                 True
initial_seasons.0            -5120.3380                  s.0                 True
initial_seasons.1            -12205.996                  s.1                 True
initial_seasons.2             3994.8531                  s.2                 True
initial_seasons.3            -7406.4652                  s.3                 True
initial_seasons.4            -1434.9389                  s.4                 True
initial_seasons.5            -11860.636                  s.5                 True
initial_seasons.6            -8847.0889                  s.6                 True
initial_seasons.7            -466.29892                  s.7                 True
initial_seasons.8             12652.704                  s.8                 True
initial_seasons.9            -5859.4752                  s.9                 True
initial_seasons.10            14524.191                 s.10                 True
initial_seasons.11            16831.461                 s.11                 True
---------------------------------------------------------------------------------


# plot daily sales
ax = df_winter.plot(figsize = (12, 4))
ax.set_ylabel('Daily Number of Sales')
ax.set_xlabel('Date')
plt.show()


# Calculate R-squared score for the test data
r2_test = 1 - np.sum((test - forecast[:len(test)]) ** 2) / np.sum((test - np.mean(test)) ** 2)
print(f"R-squared score for test set: {r2_test:.2f}")

# Calculate R-squared score for the training (fitted) data
r2_train = 1 - np.sum((train - fitted) ** 2) / np.sum((train - np.mean(train)) ** 2)
print(f"R-squared score for training (fitted) set: {r2_train:.2f}")

R-squared score for test set: 0.35
R-squared score for training (fitted) set: 0.56


# Get the actual values from the testing set
actual_values = test

# Calculate RMSE
rmse = np.sqrt(mean_squared_error(actual_values, forecast[:len(actual_values)]))

# Calculate MAPE
mape = np.mean(np.abs((actual_values - forecast[:len(actual_values)]) / actual_values)) * 100

print("RMSE:", rmse)
print("MAPE%:", mape)

RMSE: 12779.175488323612
MAPE%: 18.966745258653077


df_copy = df_1[['Order Date', 'Sales']].copy()
df_copy.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9800 entries, 0 to 9799
Data columns (total 2 columns):
 #   Column      Non-Null Count  Dtype         
---  ------      --------------  -----         
 0   Order Date  9800 non-null   datetime64[ns]
 1   Sales       9800 non-null   float64       
dtypes: datetime64[ns](1), float64(1)
memory usage: 153.2 KB


# Aggregate daily sales
time_sales = df_copy.groupby("Order Date").sum()
time_sales.head(2)


# reset the index
time_sales = time_sales.copy()
time_sales.reset_index(inplace=True)

# Resample the data on 'Sales' price monthly
df_sarima = time_sales.resample('M', on='Order Date').mean()
df_sarima.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 48 entries, 2015-01-31 to 2018-12-31
Freq: M
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Sales   48 non-null     float64
dtypes: float64(1)
memory usage: 768.0 bytes


plt.figure(figsize=(15, 4))  
sns.lineplot(x='Order Date', y='Sales', data=df_sarima)
plt.title('Monthly Sales')
plt.show()


from pmdarima import auto_arima
import statsmodels.api as sm
from statsmodels.tsa.statespace.sarimax import SARIMAX


# Perform auto_arima on the selected column
model_param = auto_arima(df_sarima, seasonal=True, m=12, trace=True)

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(1,0,1)[12] intercept   : AIC=inf, Time=0.37 sec
 ARIMA(0,1,0)(0,0,0)[12] intercept   : AIC=743.010, Time=0.01 sec
 ARIMA(1,1,0)(1,0,0)[12] intercept   : AIC=734.179, Time=0.07 sec
 ARIMA(0,1,1)(0,0,1)[12] intercept   : AIC=inf, Time=0.12 sec
 ARIMA(0,1,0)(0,0,0)[12]             : AIC=741.043, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[12] intercept   : AIC=732.556, Time=0.04 sec
 ARIMA(1,1,0)(0,0,1)[12] intercept   : AIC=734.227, Time=0.03 sec
 ARIMA(1,1,0)(1,0,1)[12] intercept   : AIC=inf, Time=0.23 sec
 ARIMA(2,1,0)(0,0,0)[12] intercept   : AIC=731.010, Time=0.05 sec
 ARIMA(2,1,0)(1,0,0)[12] intercept   : AIC=732.959, Time=0.10 sec
 ARIMA(2,1,0)(0,0,1)[12] intercept   : AIC=732.973, Time=0.09 sec
 ARIMA(2,1,0)(1,0,1)[12] intercept   : AIC=inf, Time=0.27 sec
 ARIMA(3,1,0)(0,0,0)[12] intercept   : AIC=732.984, Time=0.07 sec
 ARIMA(2,1,1)(0,0,0)[12] intercept   : AIC=732.931, Time=0.07 sec
 ARIMA(1,1,1)(0,0,0)[12] intercept   : AIC=inf, Time=0.09 sec
 ARIMA(3,1,1)(0,0,0)[12] intercept   : AIC=inf, Time=0.17 sec
 ARIMA(2,1,0)(0,0,0)[12]             : AIC=729.523, Time=0.04 sec
 ARIMA(2,1,0)(1,0,0)[12]             : AIC=731.421, Time=0.07 sec
 ARIMA(2,1,0)(0,0,1)[12]             : AIC=731.443, Time=0.07 sec
 ARIMA(2,1,0)(1,0,1)[12]             : AIC=inf, Time=0.21 sec
 ARIMA(1,1,0)(0,0,0)[12]             : AIC=730.864, Time=0.01 sec
 ARIMA(3,1,0)(0,0,0)[12]             : AIC=731.480, Time=0.05 sec
 ARIMA(2,1,1)(0,0,0)[12]             : AIC=731.386, Time=0.05 sec
 ARIMA(1,1,1)(0,0,0)[12]             : AIC=727.464, Time=0.03 sec
 ARIMA(1,1,1)(1,0,0)[12]             : AIC=728.223, Time=0.09 sec
 ARIMA(1,1,1)(0,0,1)[12]             : AIC=728.466, Time=0.10 sec
 ARIMA(1,1,1)(1,0,1)[12]             : AIC=inf, Time=0.19 sec
 ARIMA(0,1,1)(0,0,0)[12]             : AIC=726.162, Time=0.02 sec
 ARIMA(0,1,1)(1,0,0)[12]             : AIC=727.167, Time=0.06 sec
 ARIMA(0,1,1)(0,0,1)[12]             : AIC=727.419, Time=0.06 sec
 ARIMA(0,1,1)(1,0,1)[12]             : AIC=inf, Time=0.26 sec
 ARIMA(0,1,2)(0,0,0)[12]             : AIC=727.629, Time=0.02 sec
 ARIMA(1,1,2)(0,0,0)[12]             : AIC=729.062, Time=0.07 sec
 ARIMA(0,1,1)(0,0,0)[12] intercept   : AIC=inf, Time=0.04 sec

Best model:  ARIMA(0,1,1)(0,0,0)[12]          
Total fit time: 3.252 seconds


# Define SARIMA model hyperparameters, defined after fine tuning
order = (0, 1, 1)  # (p, d, q) # Non-seasonal order
seasonal_order = (0, 0, 0, 12)  # (P, D, Q, S) - Seasonal order


# Initialize the SARIMA model
sarima_model = sm.tsa.SARIMAX(df_sarima, order=order, seasonal_order=seasonal_order, enforce_stationarity=False, enforce_invertibility=False)

# Fit the SARIMA model to all of the actual data
result = sarima_model.fit()


# Print the summary of the model
print(result.summary())

# plot diagnostics to check for any unusual behavior in the model residuals
result.plot_diagnostics(figsize=(15, 12))
plt.show()

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  Sales   No. Observations:                   48
Model:               SARIMAX(0, 1, 1)   Log Likelihood                -343.687
Date:                Sun, 12 May 2024   AIC                            691.374
Time:                        21:05:06   BIC                            694.987
Sample:                    01-31-2015   HQIC                           692.721
                         - 12-31-2018                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ma.L1         -0.7197      0.107     -6.716      0.000      -0.930      -0.510
sigma2      2.506e+05   6.19e+04      4.048      0.000    1.29e+05    3.72e+05
===================================================================================
Ljung-Box (L1) (Q):                   0.51   Jarque-Bera (JB):                 1.48
Prob(Q):                              0.47   Prob(JB):                         0.48
Heteroskedasticity (H):               1.06   Skew:                             0.25
Prob(H) (two-sided):                  0.91   Kurtosis:                         2.27
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).


# Make out-of-sample forecasts for the following twelve months
forecast_extended_index = pd.date_range(start=df_sarima.index[-1], periods=12, freq='M')
forecast_extended = result.get_forecast(steps=12, index=forecast_extended_index)

# Set the first value of the extended forecast to be the last value of the actual data
forecast_extended.predicted_mean[0] = df_sarima.iloc[-1]

# Calculate R-squared score for the fitted data
fitted = result.fittedvalues  # Fitted values for all the actual data



# Plot the actual data, fitted data, and extended forecast
plt.figure(figsize=(12, 6))
plt.plot(df_sarima.index, df_sarima, label='Actual Data', color='green')
plt.plot(df_sarima.index, fitted, label='Fitted Data', color='blue')
plt.plot(forecast_extended_index, forecast_extended.predicted_mean, label='Extended Forecast', color='orange')

plt.legend()
plt.xlabel('Order Date (Monthly)')
plt.ylabel('Sales')
plt.title('SARIMA Model Forecast')
plt.show()


# Calculate R-squared for the fitted data
# Calculate the total sum of squares (TSS)
TSS = ((df_sarima - df_sarima.mean()) ** 2).sum()

# Calculate the residual sum of squares (RSS)
RSS = (result.resid ** 2).sum()

# Calculate R-squared
R_squared = 1 - (RSS / TSS)

print("R-squared:", R_squared)

R-squared: Sales   -0.0194
dtype: float64


# Access AIC and BIC values
aic = result.aic
bic = result.bic

print("AIC:", aic)
print("BIC:", bic)

AIC: 691.3737100820258
BIC: 694.9870350615664


# Calculate RMSE
rmse = np.sqrt(mean_squared_error(df_sarima, fitted))

# Calculate MAPE
def mean_absolute_percentage_error(y_true, y_pred):
    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100

mape = mean_absolute_percentage_error(df_sarima, fitted)

print("Root Mean Squared Error (RMSE):", rmse)
print("Mean Absolute Percentage Error (MAPE):", mape)

Root Mean Squared Error (RMSE): 564.8639768043034
Mean Absolute Percentage Error (MAPE): 2015-01-31 00:00:00   NaN
2015-02-28 00:00:00   NaN
2015-03-31 00:00:00   NaN
2015-04-30 00:00:00   NaN
2015-05-31 00:00:00   NaN
2015-06-30 00:00:00   NaN
2015-07-31 00:00:00   NaN
2015-08-31 00:00:00   NaN
2015-09-30 00:00:00   NaN
2015-10-31 00:00:00   NaN
2015-11-30 00:00:00   NaN
2015-12-31 00:00:00   NaN
2016-01-31 00:00:00   NaN
2016-02-29 00:00:00   NaN
2016-03-31 00:00:00   NaN
2016-04-30 00:00:00   NaN
2016-05-31 00:00:00   NaN
2016-06-30 00:00:00   NaN
2016-07-31 00:00:00   NaN
2016-08-31 00:00:00   NaN
2016-09-30 00:00:00   NaN
2016-10-31 00:00:00   NaN
2016-11-30 00:00:00   NaN
2016-12-31 00:00:00   NaN
2017-01-31 00:00:00   NaN
2017-02-28 00:00:00   NaN
2017-03-31 00:00:00   NaN
2017-04-30 00:00:00   NaN
2017-05-31 00:00:00   NaN
2017-06-30 00:00:00   NaN
2017-07-31 00:00:00   NaN
2017-08-31 00:00:00   NaN
2017-09-30 00:00:00   NaN
2017-10-31 00:00:00   NaN
2017-11-30 00:00:00   NaN
2017-12-31 00:00:00   NaN
2018-01-31 00:00:00   NaN
2018-02-28 00:00:00   NaN
2018-03-31 00:00:00   NaN
2018-04-30 00:00:00   NaN
2018-05-31 00:00:00   NaN
2018-06-30 00:00:00   NaN
2018-07-31 00:00:00   NaN
2018-08-31 00:00:00   NaN
2018-09-30 00:00:00   NaN
2018-10-31 00:00:00   NaN
2018-11-30 00:00:00   NaN
2018-12-31 00:00:00   NaN
Sales                 NaN
dtype: float64


import matplotlib.pyplot as plt

# RMSE and MAPE values for each model
prophet_rmse = 8337.57
prophet_mape = 14.79

holt_winter_rmse = 12778.183925108271
holt_winter_mape = 18.965834617492654

sarima_rmse = 564.8639768043034
sarima_mape = 0

# Create lists for RMSE and MAPE values
models = ['Prophet', 'Holt-Winters', 'SARIMA']
rmse_values = [prophet_rmse, holt_winter_rmse, sarima_rmse]
mape_values = [prophet_mape, holt_winter_mape, sarima_mape]

# Calculate R2 values for each model
r2_values = [0.75, 0.35, -0.0194]  # Replace with actual R2 values

# Model names for the legend
model_names = ['Prophet', 'Holt_Winter', 'Sarima']  # Replace with actual model names

# Create a single figure with three subplots
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4))

# Plot RMSE
ax1.bar(models, rmse_values, color='blue')
ax1.set_title('RMSE Comparison')
ax1.set_xlabel('Models')
ax1.set_ylabel('RMSE')

# Plot MAPE
ax2.bar(models, mape_values, color='green')
ax2.set_title('MAPE Comparison')
ax2.set_xlabel('Models')
ax2.set_ylabel('MAPE (%)')

# Plot R2
ax3.bar(model_names, r2_values, color=['blue', 'orange', 'green'])
ax3.set_xlabel('Model')
ax3.set_ylabel('R-squared (R2) Score')
ax3.set_title('R-squared (R2) Comparison')
ax3.set_ylim(0, 1)
ax3.grid(axis='y', linestyle='--', alpha=0.7)

# Adjust spacing between subplots
plt.subplots_adjust(wspace=0.4)

plt.show()

	Row ID	Postal Code	Sales
count	9800.000000	9789.000000	9800.000000
mean	4900.500000	55273.322403	230.769059
std	2829.160653	32041.223413	626.651875
min	1.000000	1040.000000	0.444000
25%	2450.750000	23223.000000	17.248000
50%	4900.500000	58103.000000	54.490000
75%	7350.250000	90008.000000	210.605000
max	9800.000000	99301.000000	22638.480000

	Sales
Month-Year
Apr-2015	24710.0160
Apr-2016	38056.9685
Apr-2017	42368.0480
Apr-2018	37849.2156
Aug-2015	37349.2655

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	State	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales
0	1	CA-2017-152156	8/11/2017	11/11/2017	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	Kentucky	42420.0	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600
1	2	CA-2017-152156	8/11/2017	11/11/2017	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	Kentucky	42420.0	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400
2	3	CA-2017-138688	12/6/2017	16/6/2017	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	California	90036.0	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200
3	4	US-2016-108966	11/10/2016	18/10/2016	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	Florida	33311.0	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775
4	5	US-2016-108966	11/10/2016	18/10/2016	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	Florida	33311.0	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9795	9796	CA-2017-125920	21/5/2017	28/5/2017	Standard Class	SH-19975	Sally Hughsby	Corporate	United States	Chicago	Illinois	60610.0	Central	OFF-BI-10003429	Office Supplies	Binders	Cardinal HOLDit! Binder Insert Strips,Extra St...	3.7980
9796	9797	CA-2016-128608	12/1/2016	17/1/2016	Standard Class	CS-12490	Cindy Schnelling	Corporate	United States	Toledo	Ohio	43615.0	East	OFF-AR-10001374	Office Supplies	Art	BIC Brite Liner Highlighters, Chisel Tip	10.3680
9797	9798	CA-2016-128608	12/1/2016	17/1/2016	Standard Class	CS-12490	Cindy Schnelling	Corporate	United States	Toledo	Ohio	43615.0	East	TEC-PH-10004977	Technology	Phones	GE 30524EE4	235.1880
9798	9799	CA-2016-128608	12/1/2016	17/1/2016	Standard Class	CS-12490	Cindy Schnelling	Corporate	United States	Toledo	Ohio	43615.0	East	TEC-PH-10000912	Technology	Phones	Anker 24W Portable Micro USB Car Charger	26.3760
9799	9800	CA-2016-128608	12/1/2016	17/1/2016	Standard Class	CS-12490	Cindy Schnelling	Corporate	United States	Toledo	Ohio	43615.0	East	TEC-AC-10000487	Technology	Accessories	SanDisk Cruzer 4 GB USB Flash Drive	10.3840

	Country	City	State	Postal Code
2234	United States	Burlington	Vermont	NaN
5274	United States	Burlington	Vermont	NaN
8798	United States	Burlington	Vermont	NaN
9146	United States	Burlington	Vermont	NaN
9147	United States	Burlington	Vermont	NaN
9148	United States	Burlington	Vermont	NaN
9386	United States	Burlington	Vermont	NaN
9387	United States	Burlington	Vermont	NaN
9388	United States	Burlington	Vermont	NaN
9389	United States	Burlington	Vermont	NaN
9741	United States	Burlington	Vermont	NaN

	Year	Month	Sales
0	2015	April	24710.0160
1	2015	August	37349.2655
2	2015	December	63568.3107
3	2015	February	12588.4840
4	2015	January	28828.2540
5	2015	July	35194.5580
6	2015	June	29181.3346
7	2015	March	54027.6920
8	2015	May	29520.4900
9	2015	November	64369.4565
10	2015	October	34561.9470
11	2015	September	65956.3998
12	2016	April	38056.9685
13	2016	August	49076.9300
14	2016	December	52891.8832
15	2016	February	20728.3520
16	2016	January	29347.3864
17	2016	July	28573.3100
18	2016	June	28515.9082
19	2016	March	34489.6776
20	2016	May	30761.5585
21	2016	November	50009.1450
22	2016	October	31631.8890
23	2016	September	65352.9970
24	2017	April	42368.0480
25	2017	August	45766.8144
26	2017	December	72847.0855
27	2017	February	48907.5900
28	2017	January	38048.1840
29	2017	July	41761.9430
30	2017	June	37424.6810
31	2017	March	48990.1410
32	2017	May	64836.2518
33	2017	November	66392.5470
34	2017	October	52156.9580
35	2017	September	40692.3063
36	2018	April	37849.2156
37	2018	August	75408.7840
38	2018	December	56959.2358
39	2018	February	48928.8334
40	2018	January	59767.0910
41	2018	July	53942.7755
42	2018	June	46912.8475
43	2018	March	74748.6238
44	2018	May	40882.4464
45	2018	November	87997.6400
46	2018	October	65501.1622
47	2018	September	73153.3640

	Order Date	Sales
0	2017-08-11	261.9600
1	2017-08-11	731.9400
2	2017-12-06	14.6200
3	2016-11-10	957.5775
4	2016-11-10	22.3680
...	...	...
9795	2017-05-21	3.7980
9796	2016-12-01	10.3680
9797	2016-12-01	235.1880
9798	2016-12-01	26.3760
9799	2016-12-01	10.3840

	ds	y
0	2015-01-31	28828.2540
1	2015-02-28	12588.4840
2	2015-03-31	54027.6920
3	2015-04-30	24710.0160
4	2015-05-31	29520.4900
5	2015-06-30	29181.3346
6	2015-07-31	35194.5580
7	2015-08-31	37349.2655
8	2015-09-30	65956.3998
9	2015-10-31	34561.9470
10	2015-11-30	64369.4565
11	2015-12-31	63568.3107
12	2016-01-31	29347.3864
13	2016-02-29	20728.3520
14	2016-03-31	34489.6776
15	2016-04-30	38056.9685
16	2016-05-31	30761.5585
17	2016-06-30	28515.9082
18	2016-07-31	28573.3100
19	2016-08-31	49076.9300
20	2016-09-30	65352.9970
21	2016-10-31	31631.8890
22	2016-11-30	50009.1450
23	2016-12-31	52891.8832
24	2017-01-31	38048.1840
25	2017-02-28	48907.5900
26	2017-03-31	48990.1410
27	2017-04-30	42368.0480
28	2017-05-31	64836.2518
29	2017-06-30	37424.6810
30	2017-07-31	41761.9430
31	2017-08-31	45766.8144
32	2017-09-30	40692.3063
33	2017-10-31	52156.9580
34	2017-11-30	66392.5470
35	2017-12-31	72847.0855
36	2018-01-31	59767.0910
37	2018-02-28	48928.8334
38	2018-03-31	74748.6238
39	2018-04-30	37849.2156
40	2018-05-31	40882.4464
41	2018-06-30	46912.8475
42	2018-07-31	53942.7755
43	2018-08-31	75408.7840
44	2018-09-30	73153.3640
45	2018-10-31	65501.1622
46	2018-11-30	87997.6400
47	2018-12-31	56959.2358

Loading the Necessary Libraries¶

Loading the Dataset¶

Data Description¶

Note:¶

Data Manipulation¶

Explaratory Data Analysis and Time Series Analysis¶

Showing the Categorical column in plot¶

Note:¶

1. Which category of products sells the most?¶

Note:¶

3. Which category of shipping contains the highest percentage ratio?¶

Note¶

This is an interactive treemap chart that shows the linkage between Shipping Mode, Category and Sub Category¶

4. Shown total sales by each states in the US¶

Note:¶

Note:¶

5. What is the Yearly and Monthly Sales distribution trend over the years (2015- 2018)¶¶

Note:¶

6. How are the sales trending over time? Are there any noticeable seasonal patterns?¶¶

Note:¶

7. Are there any specific regions where sales are consistently lower? What factors might be contributing to this?¶

Note:¶

8. What is the average daily sales?¶

Note:¶

7. What Product sells the most?¶

Note:¶

Decomposition of Time Series¶

Adfuller test¶

Auto-Correlation and Partial Auto- Correlation¶

Time Series Forecasting With Facebook's Prophet¶¶

Plot Forecast¶

RSME, MAPE AND R-Square for Prophet Model¶

Modelling with Holt-Winters(Additive Exponential Smoothing)¶

Fine Tuning Holt Winters¶

Note:¶

R-Square score for Holt Winter¶

RSME & MAPE SCORE for Holt Winter¶

Modelling with SARIMA¶

Fine Tuning SARIMA¶

Evaluation Metrics using RSME & MAPE¶

Evaluation Metrics Comparison¶

CONCLUSION¶